To cope with the social issue, we propose the first Korean corpus annotated for toxic speech detection. Specifically, our dataset consists of 9.4K comments from Korean online entertainment news articles.
Each comment is annotated on two aspects, the existence of social bias and hate speech, given that hate speech is closely related to bias [1]}, [2]}, [3]}.
Considering the context of Korean entertainment news where public figures encounter stereotypes mostly intertwined with gender, we weigh more on the prevalent bias. For hate speech, our label categorization refers that of [3]}, namely hate, offensive, and none.
